Sorting data

When we sort data, we arrange it in a specific sequence. This specific sequence typically helps us to spot patterns very quickly. To sort a dataset, we usually must specify one or more columns to sort by and specify the order to sort by (ascending or descending order).

In pandas, the sort_values method can be used to sort a dataset.

Getting ready

We will work with the Marketing Campaign data (https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis) for this recipe. Alternatively, you can retrieve this from the GitHub repository.

How to do it…

We will sort data using the pandas library:

1. Import the pandas library:

   import pandas as pd

2. Load the .csv file into a dataframe using read_csv. Then, subset the dataframe to include only relevant columns:

   marketing_data = pd.read_csv("data/marketing_campaign.csv")

   marketing_data = marketing_data[['ID','Year_Birth', 'Education','Marital_Status','Income','Kidhome', 'Teenhome', 'Dt_Customer',              'Recency','NumStorePurchases', 'NumWebVisitsMonth']]

3. Inspect the data. Check the first few rows and use transpose (T) to show more information. Also, check the data types as well as the number of columns and rows:

   marketing_data.head(2).T

               0    1

   ID    5524    2174

   Year_Birth    1957    1954

   Education    Graduation    Graduation

   …        …        …

   NumWebVisitsMonth    7    5

   marketing_data.dtypes

   ID    int64

   Year_Birth    int64

   Education    object

   …          …

   NumWebVisitsMonth    int64

   marketing_data.shape

   (2240, 11)

4. Sort customers based on the number of store purchases in descending order:

   sorted_data = marketing_data.sort_values('NumStorePurchases', ascending=False)

5. Inspect the result. Subset for relevant columns:

   sorted_data[['ID','NumStorePurchases']]

       ID    NumStorePurchases

   1187    9855    13

   803    9930    13

   1144    819    13

   286    10983    13

   1150    1453    13

    ...     ...    ...

   164    8475    0

   2214    9303    0

   27    5255    0

   1042    10749    0

   2132    11181    0

Great! We have sorted our dataset.

How it works...

We refer to pandas as pd in step 1. In step 2, we use read_csv to load the .csv file into a pandas dataframe and call it marketing_data. We also subset the dataframe to include only 11 relevant columns. In step 3, we inspect the dataset using head(2) to see the first two rows in the dataset; we also use transpose (T) along with head to transform the rows into columns due to the size of the data (i.e., it has many columns). We use the dtypes attribute of the dataframe to show the data types of all columns. Numeric data has int and float data types while character data has the object data type. We inspect the number of rows and columns using shape, which returns a tuple that displays the number of rows as the first element and the number of columns as the second element.

In step 4, we apply the sort_values method to sort the NumStorePurchases column. Using the sort values method, we sort NumStorePurchases in descending order. The method takes two arguments, the dataframe column to be sorted and the sorting order. false indicates a sort in descending order while true indicates a sort in ascending order.

There’s more...

Sorting can be done across multiple columns in pandas. We can sort based on multiple columns by supplying columns as a list in the sort_values method. The sort will be performed in the order in which the columns are supplied – that is, column 1 first, then column 2 next, and subsequent columns. Also, a sort isn’t limited to numerical columns alone; it can be used for columns containing characters.